Google has a neat tool that lets you quickly build a local keyword list by inputting your zip code. Google also allows you to use implicit intent as part of the search query, which can be really helpful if you’re trying to target certain keywords in an ad campaign or something like that. I took this technique and tweaked it for how people might want to find businesses in their area without spending hours typing potential searches into Google Search Console
The “semrush intent feature” is a powerful tool that helps marketers build local keyword lists. The tool also has implicit local intent, which means you can use it to find keywords with the highest possible local intent.
Anyone who has ever ran a local SEO campaign has encountered a specific issue. We must first assess if a query has local intent before we can begin setting up our rank tracking. Because there hasn’t been a straightforward method to identify local intent before recently, this procedure may be quite time consuming for big accounts.
Local SEOs have previously used either a manual technique (Google each query to check whether local websites are ranking) or geo-modifiers to big-head phrases with a lot of traffic and monitored by explicit local purpose to uncover keywords with implicit local intent. These two forms of local inquiries are highly distinct and represent various types of searcher behavior, in case you were wondering.
The Difference Between Implicit and Explicit Intent
Let’s speak about implicit vs. explicit local purpose and what it means before we go any further. A term like “moving business” might imply implicit local purpose. Even if the searcher does not expressly declare that they are seeking for local results, Google applies local results to this sort of search. Consider this a shortcut search in which the user’s location is critical to the Google results.
When someone searches a term like “moving business Denver,” they are expressing explicit local intent. The searcher has specifically requested results for a certain location, and Google will provide results tailored to that location.
If someone is doing research from home and knows they don’t need a local modifier to obtain local results, they may merely search “moving firm.” When, on the other hand, would someone seek explicitly? Many individuals use a geo-modifier as part of their daily routine, while others may use them if they wish to dial into a certain location (for example, if someone wants to join a gym near to work but is studying from home).
When someone is on vacation or on a journey and needs to find a service in their hometown, they will employ a geo-modifier. If they’re at home and need to book a hotel in a different city…well, you get the idea; consumers don’t always want results for companies in their immediate vicinity.
Consider the following example.
People may also use a geo-modifier to broaden their search results. Someone living in Golden, CO, for example, would want to consider a larger list of moving firms than what they would obtain from local and tailored results. Even if they want a service offered to their house in Golden, the searcher in Golden may adjust their search to “moving business Denver.”
The use of implicit local queries is on the increase. People are becoming used to searching without a geo-modifier and discovering that relevant results may still be found.
Webinomy’s keyword magic tool makes mapping for implicit local purpose much easier, although it does need some understanding of your sector and competition. I’d want to utilize a home services category for this example, so we’ll concentrate on Pest Control.
First, you’ll need to figure out what your primary pest control head words are. Simply putting “pest control” into Webinomy returns 92,804 keywords, with “pest control” ranking first with 110,000 monthly searches. That is wonderful. Then, to further narrow it down, apply some filters. I got 2,416 keywords if I use a minimum of 100 searches per month in the volume filter. Then, under SERP features, I’ll use the “local pack” filter, which gives me 1,320 keywords to deal with.
The first step in narrowing down your search is to apply filters.
You’ve mapped out which keywords activate the map pack at this point, but there’s some information in there that you don’t need. You probably don’t want to monitor “orkin pest control,” for example, unless you’re Orkin. Spend some time going through the different findings and entering your keywords into the Keyword Analyzer. Click “Send to other tools” in the Keyword Analyzer to send them all to the position tracking tool.
You’ll submit your queries to the position monitoring tool after you’ve gone through the Keyword Analyzer tool and done a final qualifying of them.
This is where you’ll need to know a little about your industry and competition. If your specialty is in-home services, you should be able to find your rivals that score highly for implicit local keywords by conducting some short keyword analysis study. You can use the competitor discovery feature in the position tracking tool to open up your current keyword list with the local pack everywhere and manually check for the big sites that are showing up in the local organic results, or you can use the competitor discovery feature in the position tracking tool to open up your current keyword list with the local pack everywhere and manually check for the big sites that are showing up in the local organic results (I find this works really well most of the time).
Keep in mind that the local pack and organic results use different algorithms. Even if the results below it are not localized, the local pack may display. We’re trying to figure out which searches result in localized results that are below the local pack. That’s why we’re searching for rivals that score high in organic local search results. Homeadvisor.com, yelp.com, and thumbtack.com are just a few of the larger ones that excel at this. There are more, but we’ll stick to these three for now.
The competitor discovery tool is useful if you’re having problems locating rivals that score high for implicit local searches.
Now comes the exciting part. After you’ve added these rivals, go to the Rankings Distribution panel to check how many terms from your keyword set your local competitors are ranking for. You can see that these rivals rank for some of your keyword set’s individual terms, as well as others that overlap. We’re going to assume that if they’re in the top 20, they have some kind of implicit local purpose. To check which keywords are ranking, click on each of the highlighted links.
Once you’ve entered the keyword set, add a tag to make it easier to discover those terms later. I prefer to use the tag “implicit local” to make the classification of such terms more apparent.
It’s as simple as 1, 2, 3 to add tags. Apply your tag, then click actions.
Rinse and process for each implicit local keyword competition you’ve discovered as a participant in your area. Once you have things mapped out, return to the Overview section and apply your tag to check how you rank for implicit local keywords in comparison to your rivals. This method is useful since it allows you to identify inflection points and monitor your progress for these queries over time.
This is beneficial on a variety of ways.
I’m not going to walk you through the process of mapping explicit local keywords in this tutorial since it’s so simple, and I assume most of us utilize it. If you’d want to see how I approach that process, leave a comment below and I’ll create another how-to for you.
There are various drawbacks to this method. Your recognized rivals may not be ranking for a high number of implicit local keywords, resulting in lost chances. You could also come across some strange split-intent requests. Google may also be evaluating terms on a daily basis, and your query may have local intent one day and not the next.
There’s also the idea of latent intent, which suggests that Google got the intent wrong or that there’s an intent that hasn’t been satisfied yet for a given query, or that the intent will change over time due to seasonality or current events, and that Google will eventually catch up and rank different results. That will be missed by this procedure.
It’s also possible that you’ll wind up with a tiny keyword collection with low volume. This might be the case in several sectors. In certain circumstances, you may need to extend the keywords you began with by starting again and conducting this analysis without first filtering down to the local pack queries. Because adding all 94,000+ keywords to position tracking (and doing so for every customer) is not financially feasible, I made the assumption that most results with local organic results would also contain a map pack. Another way is to add other head phrases to your list, such as “exterminator” instead of “pest control,” and see if it adds more inquiries.
Ultimately, the goal of this method is to increase efficiency in the local keyword area so you can spend less time researching and more time improving content and off-page signals. Local intent inquiries, in my experience, are often dependable since the intent tends to stay local and does not change frequently. I’ve used this method in ecommerce (for groceries), so I’m certain it’ll work in other sectors as well. Overall, this technique has made my keyword research more efficient, and I hope it does the same for you.
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